Social Media Mining with R


Social Media Mining with R
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Overview
Table of Contents
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  • Learn how to face the challenges of analyzing social media data
  • Get hands-on experience with the most common, up-to-date sentiment analysis tools and apply them to data collected from social media websites through a series of in-depth case studies, which includes how to mine Twitter data
  • A focused guide to help you achieve practical results when interpreting social media data

Book Details

Language : English
Paperback : 122 pages [ 235mm x 191mm ]
Release Date : March 2014
ISBN : 1783281774
ISBN 13 : 9781783281770
Author(s) : Richard Heimann, Nathan Danneman
Topics and Technologies : All Books, Big Data and Business Intelligence, Open Source


Table of Contents

Preface
Chapter 1: Going Viral
Chapter 2: Getting Started with R
Chapter 3: Mining Twitter with R
Chapter 4: Potentials and Pitfalls of Social Media Data
Chapter 5: Social Media Mining – Fundamentals
Chapter 6: Social Media Mining – Case Studies
Appendix: Conclusions and Next Steps
Index
  • Chapter 1: Going Viral
    • Social media mining using sentiment analysis
    • The state of communication
    • What is Big Data?
    • Human sensors and honest signals
    • Quantitative approaches
    • Summary
  • Chapter 2: Getting Started with R
    • Why R?
    • Quick start
      • The basics – assignment and arithmetic
      • Functions, arguments, and help
    • Vectors, sequences, and combining vectors
    • A quick example – creating data frames and importing files
    • Visualization in R
    • Style and workflow
    • Additional resources
    • Summary
  • Chapter 5: Social Media Mining – Fundamentals
    • Key concepts of social media mining
    • Good data versus bad data
    • Understanding sentiments
      • Scherer's typology of emotions
    • Sentiment polarity – data and classification
    • Supervised social media mining – lexicon-based sentiment
    • Supervised social media mining – Naive Bayes classifiers
    • Unsupervised social media mining – Item Response Theory for text scaling
    • Summary
  • Chapter 6: Social Media Mining – Case Studies
    • Introductory considerations
    • Case study 1 – supervised social media mining – lexicon-based sentiment
    • Case study 2 – Naive Bayes classifier
    • Case study 3 – IRT models for unsupervised sentiment scaling
    • Summary

Richard Heimann

Richard Heimann leads the Data Science Team at Data Tactics Corporation and is an EMC Certified Data Scientist specializing in spatial statistics, data mining, Big Data, and pattern discovery and recognition. Since 2005, Data Tactics has been a premier Big Data and analytics service provider based in Washington D.C., serving customers globally.

Richard is an adjunct faculty member at the University of Maryland, Baltimore County, where he teaches spatial analysis and statistical reasoning. Additionally, he is an instructor at George Mason University, teaching human terrain analysis, and is also a selection committee member for the 2014-2015 AAAS Big Data and Analytics Fellowship Program.

In addition to co-authoring Social Media Mining in R, Richard has also recently reviewed Making Big Data Work for Your Business for Packt Publishing, and also writes frequently on related topics for the Big Data Republic (http://www.bigdatarepublic.com/bloggers.asp#Rich_Heimann). He has recently assisted DARPA, DHS, the US Army, and the Pentagon with analytical support.


Nathan Danneman

Nathan Danneman holds a PhD degree from Emory University, where he studied International Conflict. Recently, his technical areas of research have included the analysis of textual and geospatial data and the study of multivariate outlier detection.

Nathan is currently a data scientist at Data Tactics, and supports programs at DARPA and the Department of Homeland Security.

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Sample chapters

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What you will learn from this book

  • Learn the basics of R and all the data types
  • Explore the vast expanse of social science research
  • Discover more about data potential, the pitfalls, and inferential gotchas
  • Gain an insight into the concepts of supervised and unsupervised learning
  • Familiarize yourself with visualization and some cognitive pitfalls
  • Delve into exploratory data analysis
  • Understand the minute details of sentiment analysis

In Detail

The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. However, analyzing this ever-growing pile of data is quite tricky and, if done erroneously, could lead to wrong inferences.

By using this essential guide, you will gain hands-on experience with generating insights from social media data. This book provides detailed instructions on how to obtain, process, and analyze a variety of socially-generated data while providing a theoretical background to help you accurately interpret your findings. You will be shown R code and examples of data that can be used as a springboard as you get the chance to undertake your own analyses of business, social, or political data.

The book begins by introducing you to the topic of social media data, including its sources and properties. It then explains the basics of R programming in a straightforward, unassuming way. Thereafter, you will be made aware of the inferential dangers associated with social media data and how to avoid them, before describing and implementing a suite of social media mining techniques.

Social Media Mining in R provides a light theoretical background, comprehensive instruction, and state-of-the-art techniques, and by reading this book, you will be well equipped to embark on your own analyses of social media data.

Approach

A concise, hands-on guide with many practical examples and a detailed treatise on inference and social science research that will help you in mining data in the real world.

Who this book is for

Whether you are an undergraduate who wishes to get hands-on experience working with social data from the Web, a practitioner wishing to expand your competencies and learn unsupervised sentiment analysis, or you are simply interested in social data analysis, this book will prove to be an essential asset. No previous experience with R or statistics is required, though having knowledge of both will enrich your experience.

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